As a result of hardware miniaturization and ubiquitous internet access, there is a wealth of user- and sensor-generated geographic content available, from social media activity to private weather stations to embedded sensor technology for managing public transport or optimizing energy consumption. Using crowdsourcing and user-generated content have become important methodologies in today's data-driven research, and the domain of geographic information science is no exception.

A crucial component of any research involving volunteered or crowdsourced geographic information is semantics - since contributions from individual citizens have a degree of subjectivity, understanding the different meanings people associate with geographic features can help to understand the attributes of a particular data point.

While a lot of research has been carried out on how to establish the semantics and how to query them (e.g. using linked geographic data in RDF triple stores and querying via SPARQL endpoints), much less research has been conducted on how to communicate the different meanings to an end user. Prime example is the hugely successful OpenStreetMap project: Although it is truly a crowdsourced project with millions of contributors and continuous updating and improving of data, the end product is a traditional map that shows (in the best of circumstances) a consensus or (in the worst of circumstances) the last edit of an edit war. This research topic aims to develop methods on how to build the different perspectives (if present), and how to visualize them to an end-user in a simple and user-friendly way.

Useful skills include some programming or scripting (Python, Java) for retrieving and manipulating data, basic cloud computing or web mapping skills to set up a prototype, and optionally semantic web skills (SPARQL, linked data) for contextualizing the data and improving the analysis.

Tasks and expected results
- examine existing approaches to extracting semantics from volunteered or crowdsourced geographic information (e.g. using the Ohsome API (https://wiki.openstreetmap.org/wiki/Ohsome_API ) to retrieve OSM data)
- develop a method to "mine" the data for different or conflicting perspectives
- develop a method to visualize those perspectives on top of the data itself
- develop a prototype that allows and end-user to examine those perspectives interactively

If required, there are several georeferenced social media data sets from Enschede and London available, in addition to the freely available OSM data including history.